What are the hardware requirements for Gemma 7B?

Gemma 7B requires approximately 17.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 7B and how large is it?

Gemma 7B is developed by google, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 7B perform on standard benchmarks?

Gemma 7B has the following published scores: MMLU 64.56. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 7B by google — VRAM 17.1GB, 8K context

Name: Gemma 7B
Author: google

Gemma 7B is an open-source language model by google. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, License: gemma, Quantized, HF Score: 63.8, LLM Explorer Score: 0.3, Arc: 61.1, HellaSwag: 82.2, MMLU: 64.6, TruthfulQA: 44.8, WinoGrande: 79, GSM8K: 50.9.

Model Card on HF 🤗: https://huggingface.co/google/gemma-7b

Gemma 7B Benchmarks

IFEval: 26.59 vs 88 (so35)^-69.8%

ARC: 61.09 vs 96.7 (so35)^-36.8%

HellaSwag: 82.2 vs 95.3 (gpt4)^-13.7%

MMLU: 64.56 vs 88.3 (so35)^-26.9%

TruthfulQA: 44.79 vs 59 (gpt4)^-24.1%

WinoGrande: 79.01 vs 87.5 (gpt4)^-9.7%

GSM8K: 50.87 vs 96.4 (so35)^-47.2%

MATH Lvl 5: 7.4

LLME Score: 0.29549

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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Gemma 7B Parameters and Internals

Model Type

text-to-text, decoder-only, large language models

Use Cases

Areas:

Content Creation and Communication, Research and Education

Applications:

Text Generation, Chatbots and Conversational AI, Text Summarization, Natural Language Processing Research, Language Learning Tools, Knowledge Exploration

Primary Use Cases:

Question answering, Summarization, Reasoning

Limitations:

Biases from training data, Complex task handling limitations, Figurative language and nuances issues, Factual inaccuracies

Considerations:

Continuous monitoring, content safety guidelines, and education around privacy.

Supported Languages

English (full)

Training Details

Data Sources:

Web Documents, Code, Mathematics

Data Volume:

6 trillion tokens

Context Length:

8192

Hardware Used:

TPUv5e

Model Architecture:

text-to-text, decoder-only large language model

Safety Evaluation

Methodologies:

structured evaluations, internal red-teaming

Findings:

acceptable thresholds for internal policies

Risk Categories:

Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm

Ethical Considerations:

Bias and Fairness, Misinformation, Transparency

Responsible Ai Considerations

Fairness:

The model underwent input data pre-processing and evaluations to assess socio-cultural biases.

Transparency:

Summarizes details on architecture, capabilities, and limitations.

Accountability:

Open model development aims to share innovation with developers and researchers.

Mitigation Strategies:

De-biasing techniques, content safety mechanisms, and end-user education.

Input Output

Input Format:

Text string

Accepted Modalities:

text

Output Format:

Generated English-language text

LLM Name	Gemma 7B
Repository 🤗	https://huggingface.co/google/gemma-7b
Base Model(s)	Gemma Ei Oc Structured Train Holmeister/Gemma_ei_oc_structured_train
Model Size	7b
Required VRAM	17.1 GB
Updated	2026-04-07
Maintainer	google
Model Type	gemma
Model Files	34.2 GB 5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.1 GB: 4-of-4
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	GemmaForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Quantized Models of the Gemma 7B

Model	Likes	Downloads	VRAM
Gemma 7B It	1243	38213	17 GB
Train06	0	21	9 GB
CNCF	0	12	5 GB
Gemma 7B GGUF	15	1796	3 GB

Best Alternatives to Gemma 7B

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 7B It	8K / 17.1 GB	38213	1243
Gemma 1.1 7B It GGUF	8K / 5.3 GB	128	1
Train06	8K / 9.1 GB	21	0
Llama2 Kazakh 7B GGUF	8K / 4.1 GB	10	0
Gemma 7B	8K / 17.1 GB	36	0
Gemma 7B Translator 0.4	8K / 17.1 GB	43	0
Gemma 7B Translator 0.3	8K / 17.1 GB	28	0
Gemma7B Konosuba	8K / 17.1 GB	20	1
Gemma 7B It GGUF	8K / 5.3 GB	48	1
Gemma 7B It GGUF	8K / 3.1 GB	503	11

Note: green Score (e.g. "73.2") means that the model is better than google/gemma-7b.

Rank the Gemma 7B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Gemma 7B by google